1. Technical Field
The present disclosure relates to an action recognition system and method thereof in particular, to an action recognition system operable to determine an action of a user by optically detecting the muscular variation of a movable part of the user and the method thereof.
2. Description of Related Art
Muscle is one of the important tissues in the human body and mainly classifies into three types: skeletal muscle, smooth muscle, and cardiac muscle. The skeletal muscle mainly surrounds the extremities of the human body for controlling the basic body movement. More specifically, the contraction movements of the skeletal muscle are controlled by human conscious and stimulated by the never system in a manner that the contraction movements of the skeleton muscle can cause the movement of human skeleton helping the human body to move.
The surface electromyography (SEMG) is used to detect and record the electromyographic signals generated during the contraction movements of the muscle by electrodes placed on the surface above the muscle, wherein each of the electromyographic signals generated is related to a muscular activity and thus reflects the muscular variation during muscle movement. Currently the SEMG not only is utilized for medical diagnostic use but also is utilized for action recognition application.
However, existing SEMG still has issues with action recognition operations, such as the processing speed of a electromyographic signal processing apparatus does not easily comply to the requirements for action recognition due to the limitation of field programmable gate array (FPGA), the circuitry for amplifying the signal at sensing end is relatively complex, the measuring positions are limited only to the chest and forearms, particular hands, moreover electromyographic signals are vulnerable to interference of power source noises and electromagnetic noises. In other words, there still exist many issues with present action recognition systems using SEMG. To the rapidly developed wearable technology, action recognition products using electromyographic signals are still not suitable for consumer wearable electronic product applications.